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2015 Bulletin of the Australian Mathematical Society  
Developments range from theoretical results to empirical studies, offering contributions to the literatures of finite mixture models, species distribution models, variable selection, cluster analysis and  ...  In this thesis, new methods are proposed for using finite mixture models to analyse multi-species data in ecology.  ...  Developments range from theoretical results to empirical studies, offering contributions to the literatures of finite mixture models, species distribution models, variable selection, cluster analysis and  ... 
doi:10.1017/s0004972715000945 fatcat:2uj3x7jsrzcsjixwt256et7k7e

Page 430 of Mathematical Reviews Vol. , Issue 96a [page]

1996 Mathematical Reviews  
96a:62071 62 mixture-model cluster analysis.  ...  the number of component clusters in the mixture-model cluster analysis.  ... 

Probabilistic principal component subspaces: a hierarchical finite mixture model for data visualization

Yue Wang, Lan Luo, M.T. Freedman, Sun-Yuan Kung
2000 IEEE Transactions on Neural Networks  
Index Terms-Computer-aided diagnosis, data visualization, hierarchical mixture distribution, information theoretic criteria, principal component neural network, soft clustering.  ...  component neural networks under the information theoretic criteria.  ...  Second, we impose a model selection procedure to determine the number of subclusters inside each cluster at each level using information theoretic criteria.  ... 
doi:10.1109/72.846734 pmid:18249790 fatcat:ucdmszpqg5g7titl3wizzfgrru

The importance of cluster analysis for enhancing clinical practice: an example from irritable bowel syndrome

Sula Windgassen, Rona Moss-Morris, Kimberley Goldsmith, Trudie Chalder
2018 Journal of Mental Health  
A strong empirical and/or theoretically informed basis for such analysis is vital to inform which measures are included in analysis and the extent to which clusters identified make theoretical/clinical  ...  Multidimensionality of IBS clinical profiles has been examined using mixture modelling cluster analysis, which included measures of bowel symptom type (IBS-C, IBS-D, IBS-A), symptom severity, the occurrence  ... 
doi:10.1080/09638237.2018.1437615 pmid:29447026 fatcat:64v2sclfurejjiewvkmxq2qj2y

Information Criteria's Performance in Finite Mixture Models with Mixed Features

Jaime R. S. Fonseca
2018 Journal of Scientific Research and Reports  
Thus we may conclude that AIC 3 and AICu are the best information criteria for selecting the true number of clusters when dealing with finite mixture models, mixed data and information criteria for model  ...  Aims: This study is intended to determine which information criterion is more appropriate for mixture model selection when considering data sets with both categorical and numerical clustering base variables  ...  CLUSTERING VIA FINITE MIXTURE MODELS For illustratting the use of finite mixture models in the field of cluster analysis, see for instance [2] .  ... 
doi:10.9734/jsrr/2018/9004 fatcat:4gxqmupcjngbdn2iunqmlgrxii

Special issue on "Advances on model-based clustering and classification"

Sylvia Frühwirth-Schnatter, Salvatore Ingrassia, Agustín Mayo-Iscar
2019 Advances in Data Analysis and Classification  
This Special Issue of ADAC is devoted to recent developments in Model-Based Clustering and Classification which is an increasingly active area in both theoretical and applied research.  ...  This special issue contains 13 papers, that have been accepted for publication after a blinded peer-reviewed process, dealing with quite different topics like mixture models for both continuous and discrete  ...  This likelihood-based one-mode and two-mode fuzzy clustering provides maximum likelihood estimation of parameters and the options of using likelihood information criteria for model comparison.  ... 
doi:10.1007/s11634-019-00355-w fatcat:qz4nw4vd5ralnbffs4qvb3p5ja

Market segmentation with mixture regression models: Understanding measures that guide model selection

Marko Sarstedt
2008 Journal of Targeting, Measurement and Analysis for Marketing  
criteria for assessing the correct number of segments in mixture regression models.  ...  His research interests include research methodology, especially in the fi elds of fi nite mixture modelling and partial least-squares path analysis.  ...  These chance models are derived from discriminant analysis and are used to evaluate the information criteria ' s absolute performance with respect to chance.  ... 
doi:10.1057/jt.2008.9 fatcat:6pwvof34tfhylco5ppmj2c57lq

The full Bayesian significance test for mixture models: results in gene expression clustering

M.S. Lauretto, C.A.B. Pereira, J.M. Stern
2008 Genetics and Molecular Research  
Compared to Mclust (model-based clustering), our method shows more consistent results.  ...  To decide the number of components, information criteria are commonly used.  ...  linkage criteria used.  ... 
doi:10.4238/vol7-3x-meeting06 fatcat:je643afcpbh3nmuxylmo5xp3cy

Class Evolution Tree

Levente Kriston, Hanne Melchior, Anika Hergert, Corinna Bergelt, Birgit Watzke, Holger Schulz, Alessa von Wolff
2011 International Journal of Rehabilitation Research  
In the second study, growth mixture modeling was used to identify latent trajectory classes based on weekly symptom severity measurements during inpatient treatment of patients with mental disorders.  ...  The Class Evolution Tree can be used in addition to statistical criteria to systematically address the issue of number of classes in explorative categorical latent variable modeling.  ...  The studies that were used to show applications were funded by the Wilhelm-Stiftung für Rehabilitationsforschung (Wilhelm Foundation for Rehabilitation Research, Germany).  ... 
doi:10.1097/mrr.0b013e3283460e7d pmid:21467944 fatcat:x6pz7ddvojdhhbun5bcbpysgoq

Cluster number selection for a small set of samples using the Bayesian Ying-Yang model

Ping Guo, C.L.P. Chen, M.R. Lyu
2002 IEEE Transactions on Neural Networks  
In this paper, we describe both theoretical and experimental results in determining the cluster number for a small set of samples using the Bayesian-Kullback Ying-Yang (BYY) model selection criterion.  ...  One major problem in cluster analysis is the determination of the number of clusters.  ...  In the literature, there are several heuristically proposed information theoretical criteria.  ... 
doi:10.1109/tnn.2002.1000144 pmid:18244472 fatcat:xtjecbjoojcfjcj5aynyiwondq

On determining efficient finite mixture models with compact and essential components for clustering data

Ahmed R. Abas
2013 Egyptian Informatics Journal  
In this paper, an algorithm is proposed to learn and evaluate different finite mixture models (FMMs) for data clustering using a new proposed criterion.  ...  Essential components have minimum mutual information, that is, redundancy, among them, and therefore, they have minimum overlapping among them.  ...  However, due to the dependency on the EM algorithm, the model selected using these criteria is not necessarily the best model for clustering small data sets.  ... 
doi:10.1016/j.eij.2013.02.002 fatcat:4hhc2uo6njhmhnz7ft3wzurps4

Summarizing Finite Mixture Model with Overlapping Quantification

Shunki Kyoya, Kenji Yamanishi
2021 Entropy  
Finite mixture models are widely used for modeling and clustering data. When they are used for clustering, they are often interpreted by regarding each component as one cluster.  ...  The primary purpose of this paper is to establish a theoretical framework for interpreting the overlapping mixture models by estimating how they overlap, using measures of information such as entropy and  ...  Analysis of Artificial Dataset To reveal the differences among the criteria, we conducted experiments with artificially generated Gaussian mixture models.  ... 
doi:10.3390/e23111503 pmid:34828201 pmcid:PMC8622449 fatcat:64uxyo5pl5bdxen5zhxzqvn7gy

Enhancing the selection of a model-based clustering with external categorical variables

Jean-Patrick Baudry, Margarida Cardoso, Gilles Celeux, Maria José Amorim, Ana Sousa Ferreira
2014 Advances in Data Analysis and Classification  
In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which were not directly involved to cluster the data.  ...  It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only.  ...  Whereas cluster analysis is an exploratory data analysis tool, any available information on the objects to be clustered, available in addition to the clustering variables, could be very useful to get a  ... 
doi:10.1007/s11634-014-0177-3 fatcat:5p3espfwhzffxo56uzk4der5hm

Data-driven penalty calibration: A case study for Gaussian mixture model selection

Cathy Maugis, Bertrand Michel
2011 E S A I M: Probability & Statistics  
In a model-based clustering context, the specific form of the considered Gaussian mixtures allows us to detect the noisy variables in order to improve the data clustering and its interpretation.  ...  The paper is organized as follows: Section 1 presents the collections of Gaussian mixture model used in this paper.  ...  It has been compared with the standard asymptotic criteria BIC, ICL and AIC, currently used in this Gaussian mixture clustering context.  ... 
doi:10.1051/ps/2010002 fatcat:u4bxtycngffxzlsfau2jxbzi7y

Data mapping by probabilistic modular networks and information-theoretic criteria

Yue Wang, Shang-Hung Lin, Huai Li, Sun-Yuan Kung
1998 IEEE Transactions on Signal Processing  
We adapt a model fitting scheme that determines both the number and kernel of local clusters using informationtheoretic criteria.  ...  with locally mixture clusters; 2) estimation of the data distributions for each induced cluster inside each class; 3) classification of the data into classes that realizes the data memberships.  ...  kernel shape of local clusters inside each class using information-theoretic criteria.  ... 
doi:10.1109/78.735311 fatcat:32mcfazlingbvhbqrwhwf6ubu4
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